{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6c9db044-adce-49d1-a014-88db57908b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>caption</th>\n",
       "      <th>path</th>\n",
       "      <th>gender</th>\n",
       "      <th>product_type</th>\n",
       "      <th>product_id</th>\n",
       "      <th>image_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MEN-Denim-id_00000089-28_1_front</td>\n",
       "      <td>This gentleman is wearing a medium-sleeve shir...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>MEN</td>\n",
       "      <td>Denim</td>\n",
       "      <td>id_00000089</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MEN-Denim-id_00000265-01_1_front</td>\n",
       "      <td>This person is wearing a short-sleeve shirt wi...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>MEN</td>\n",
       "      <td>Denim</td>\n",
       "      <td>id_00000265</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MEN-Denim-id_00000313-01_1_front</td>\n",
       "      <td>The gentleman is wearing a short-sleeve T-shir...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>MEN</td>\n",
       "      <td>Denim</td>\n",
       "      <td>id_00000313</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MEN-Denim-id_00000516-01_1_front</td>\n",
       "      <td>The person wears a sleeveless tank shirt with ...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>MEN</td>\n",
       "      <td>Denim</td>\n",
       "      <td>id_00000516</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MEN-Denim-id_00000750-01_1_front</td>\n",
       "      <td>His sweater has long sleeves, cotton fabric an...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>MEN</td>\n",
       "      <td>Denim</td>\n",
       "      <td>id_00000750</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12273</th>\n",
       "      <td>WOMEN-Tees_Tanks-id_00007970-01_1_front</td>\n",
       "      <td>This lady is wearing a short-sleeve shirt with...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>Tees_Tanks</td>\n",
       "      <td>id_00007970</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12274</th>\n",
       "      <td>WOMEN-Tees_Tanks-id_00007976-01_1_front</td>\n",
       "      <td>Her tank top has sleeves cut off, cotton fabri...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>Tees_Tanks</td>\n",
       "      <td>id_00007976</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12275</th>\n",
       "      <td>WOMEN-Tees_Tanks-id_00007979-03_1_front</td>\n",
       "      <td>The female wears a tank tank shirt with graphi...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>Tees_Tanks</td>\n",
       "      <td>id_00007979</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12276</th>\n",
       "      <td>WOMEN-Tees_Tanks-id_00007979-04_1_front</td>\n",
       "      <td>The tank top this person wears has sleeves cut...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>Tees_Tanks</td>\n",
       "      <td>id_00007979</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12277</th>\n",
       "      <td>WOMEN-Tees_Tanks-id_00007981-03_1_front</td>\n",
       "      <td>This woman wears a sleeveless tank top with ot...</td>\n",
       "      <td>../dataset/DeepFashion-MultiModal/selected_ima...</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>Tees_Tanks</td>\n",
       "      <td>id_00007981</td>\n",
       "      <td>front</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12278 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      image_id  \\\n",
       "0             MEN-Denim-id_00000089-28_1_front   \n",
       "1             MEN-Denim-id_00000265-01_1_front   \n",
       "2             MEN-Denim-id_00000313-01_1_front   \n",
       "3             MEN-Denim-id_00000516-01_1_front   \n",
       "4             MEN-Denim-id_00000750-01_1_front   \n",
       "...                                        ...   \n",
       "12273  WOMEN-Tees_Tanks-id_00007970-01_1_front   \n",
       "12274  WOMEN-Tees_Tanks-id_00007976-01_1_front   \n",
       "12275  WOMEN-Tees_Tanks-id_00007979-03_1_front   \n",
       "12276  WOMEN-Tees_Tanks-id_00007979-04_1_front   \n",
       "12277  WOMEN-Tees_Tanks-id_00007981-03_1_front   \n",
       "\n",
       "                                                 caption  \\\n",
       "0      This gentleman is wearing a medium-sleeve shir...   \n",
       "1      This person is wearing a short-sleeve shirt wi...   \n",
       "2      The gentleman is wearing a short-sleeve T-shir...   \n",
       "3      The person wears a sleeveless tank shirt with ...   \n",
       "4      His sweater has long sleeves, cotton fabric an...   \n",
       "...                                                  ...   \n",
       "12273  This lady is wearing a short-sleeve shirt with...   \n",
       "12274  Her tank top has sleeves cut off, cotton fabri...   \n",
       "12275  The female wears a tank tank shirt with graphi...   \n",
       "12276  The tank top this person wears has sleeves cut...   \n",
       "12277  This woman wears a sleeveless tank top with ot...   \n",
       "\n",
       "                                                    path gender product_type  \\\n",
       "0      ../dataset/DeepFashion-MultiModal/selected_ima...    MEN        Denim   \n",
       "1      ../dataset/DeepFashion-MultiModal/selected_ima...    MEN        Denim   \n",
       "2      ../dataset/DeepFashion-MultiModal/selected_ima...    MEN        Denim   \n",
       "3      ../dataset/DeepFashion-MultiModal/selected_ima...    MEN        Denim   \n",
       "4      ../dataset/DeepFashion-MultiModal/selected_ima...    MEN        Denim   \n",
       "...                                                  ...    ...          ...   \n",
       "12273  ../dataset/DeepFashion-MultiModal/selected_ima...  WOMEN   Tees_Tanks   \n",
       "12274  ../dataset/DeepFashion-MultiModal/selected_ima...  WOMEN   Tees_Tanks   \n",
       "12275  ../dataset/DeepFashion-MultiModal/selected_ima...  WOMEN   Tees_Tanks   \n",
       "12276  ../dataset/DeepFashion-MultiModal/selected_ima...  WOMEN   Tees_Tanks   \n",
       "12277  ../dataset/DeepFashion-MultiModal/selected_ima...  WOMEN   Tees_Tanks   \n",
       "\n",
       "        product_id image_type  \n",
       "0      id_00000089      front  \n",
       "1      id_00000265      front  \n",
       "2      id_00000313      front  \n",
       "3      id_00000516      front  \n",
       "4      id_00000750      front  \n",
       "...            ...        ...  \n",
       "12273  id_00007970      front  \n",
       "12274  id_00007976      front  \n",
       "12275  id_00007979      front  \n",
       "12276  id_00007979      front  \n",
       "12277  id_00007981      front  \n",
       "\n",
       "[12278 rows x 7 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取csv文件\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "# 读取CSV文件\n",
    "df = pd.read_csv('../dataset/DeepFashion-MultiModal/labels_front.csv')\n",
    "df[\"path\"] = df[\"path\"].apply(lambda x: \"../dataset/DeepFashion-MultiModal/selected_images/\"+x)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2eb84e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "数据基本信息：\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12278 entries, 0 to 12277\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   image_id      12278 non-null  object\n",
      " 1   caption       12278 non-null  object\n",
      " 2   path          12278 non-null  object\n",
      " 3   gender        12278 non-null  object\n",
      " 4   product_type  12278 non-null  object\n",
      " 5   product_id    12278 non-null  object\n",
      " 6   image_type    12278 non-null  object\n",
      "dtypes: object(7)\n",
      "memory usage: 671.6+ KB\n",
      "None\n",
      "\n",
      "数据形状：(12278, 7)\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n数据基本信息：\")\n",
    "print(df.info())\n",
    "\n",
    "# 显示数据形状\n",
    "print(f\"\\n数据形状：{df.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4c392c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "class FashionDataset(Dataset):\n",
    "    def __init__(self, df, processor):\n",
    "        self.df = df\n",
    "        self.processor = processor  # 加入 processor\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        row = self.df.iloc[idx]\n",
    "        image_path = row['path']\n",
    "        text = row['caption']\n",
    "        \n",
    "        # 读取并转换图片\n",
    "        image = Image.open(image_path).convert('RGB')\n",
    "        \n",
    "        # 使用 processor 处理图像和文本\n",
    "        encoding = self.processor(\n",
    "            images=image,\n",
    "            text=text,\n",
    "            padding='max_length',         # 或 \"do_not_pad\"，依据模型设置\n",
    "            return_tensors='pt'\n",
    "        )\n",
    "        \n",
    "        # 去除 batch 维度（默认是 batch_size=1 的形式）\n",
    "        encoding = {k: v.squeeze(0) for k, v in encoding.items()}\n",
    "        \n",
    "        return encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "bf7e2e2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片路径: ../dataset/DeepFashion-MultiModal/selected_images/MEN-Denim-id_00000089-28_1_front.jpg\n",
      "文本描述:This gentleman is wearing a medium-sleeve shirt with pure color patterns. The shirt is with cotton fabric and its neckline is lapel. This gentleman wears a long trousers. The trousers are with cotton fabric and solid color patterns.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = df[\"path\"][0]\n",
    "print(\"图片路径:\", path)\n",
    "\n",
    "# 读取并展示图片\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "# 读取图片\n",
    "img = mpimg.imread(path)\n",
    "\n",
    "print(\"文本描述:\"+df[\"caption\"][0])\n",
    "\n",
    "# 创建图形\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.imshow(img)\n",
    "plt.axis('off')  # 隐藏坐标轴\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "93072eda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BlipForImageTextRetrieval were not initialized from the model checkpoint at /root/autodl-fs/blip-image-captioning-base/ and are newly initialized: ['itm_head.bias', 'itm_head.weight', 'text_encoder.embeddings.LayerNorm.bias', 'text_encoder.embeddings.LayerNorm.weight', 'text_encoder.embeddings.position_embeddings.weight', 'text_encoder.embeddings.word_embeddings.weight', 'text_encoder.encoder.layer.0.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.0.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.0.attention.output.dense.bias', 'text_encoder.encoder.layer.0.attention.output.dense.weight', 'text_encoder.encoder.layer.0.attention.self.key.bias', 'text_encoder.encoder.layer.0.attention.self.key.weight', 'text_encoder.encoder.layer.0.attention.self.query.bias', 'text_encoder.encoder.layer.0.attention.self.query.weight', 'text_encoder.encoder.layer.0.attention.self.value.bias', 'text_encoder.encoder.layer.0.attention.self.value.weight', 'text_encoder.encoder.layer.0.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.0.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.0.crossattention.output.dense.bias', 'text_encoder.encoder.layer.0.crossattention.output.dense.weight', 'text_encoder.encoder.layer.0.crossattention.self.key.bias', 'text_encoder.encoder.layer.0.crossattention.self.key.weight', 'text_encoder.encoder.layer.0.crossattention.self.query.bias', 'text_encoder.encoder.layer.0.crossattention.self.query.weight', 'text_encoder.encoder.layer.0.crossattention.self.value.bias', 'text_encoder.encoder.layer.0.crossattention.self.value.weight', 'text_encoder.encoder.layer.0.intermediate.dense.bias', 'text_encoder.encoder.layer.0.intermediate.dense.weight', 'text_encoder.encoder.layer.0.output.LayerNorm.bias', 'text_encoder.encoder.layer.0.output.LayerNorm.weight', 'text_encoder.encoder.layer.0.output.dense.bias', 'text_encoder.encoder.layer.0.output.dense.weight', 'text_encoder.encoder.layer.1.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.1.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.1.attention.output.dense.bias', 'text_encoder.encoder.layer.1.attention.output.dense.weight', 'text_encoder.encoder.layer.1.attention.self.key.bias', 'text_encoder.encoder.layer.1.attention.self.key.weight', 'text_encoder.encoder.layer.1.attention.self.query.bias', 'text_encoder.encoder.layer.1.attention.self.query.weight', 'text_encoder.encoder.layer.1.attention.self.value.bias', 'text_encoder.encoder.layer.1.attention.self.value.weight', 'text_encoder.encoder.layer.1.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.1.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.1.crossattention.output.dense.bias', 'text_encoder.encoder.layer.1.crossattention.output.dense.weight', 'text_encoder.encoder.layer.1.crossattention.self.key.bias', 'text_encoder.encoder.layer.1.crossattention.self.key.weight', 'text_encoder.encoder.layer.1.crossattention.self.query.bias', 'text_encoder.encoder.layer.1.crossattention.self.query.weight', 'text_encoder.encoder.layer.1.crossattention.self.value.bias', 'text_encoder.encoder.layer.1.crossattention.self.value.weight', 'text_encoder.encoder.layer.1.intermediate.dense.bias', 'text_encoder.encoder.layer.1.intermediate.dense.weight', 'text_encoder.encoder.layer.1.output.LayerNorm.bias', 'text_encoder.encoder.layer.1.output.LayerNorm.weight', 'text_encoder.encoder.layer.1.output.dense.bias', 'text_encoder.encoder.layer.1.output.dense.weight', 'text_encoder.encoder.layer.10.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.10.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.10.attention.output.dense.bias', 'text_encoder.encoder.layer.10.attention.output.dense.weight', 'text_encoder.encoder.layer.10.attention.self.key.bias', 'text_encoder.encoder.layer.10.attention.self.key.weight', 'text_encoder.encoder.layer.10.attention.self.query.bias', 'text_encoder.encoder.layer.10.attention.self.query.weight', 'text_encoder.encoder.layer.10.attention.self.value.bias', 'text_encoder.encoder.layer.10.attention.self.value.weight', 'text_encoder.encoder.layer.10.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.10.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.10.crossattention.output.dense.bias', 'text_encoder.encoder.layer.10.crossattention.output.dense.weight', 'text_encoder.encoder.layer.10.crossattention.self.key.bias', 'text_encoder.encoder.layer.10.crossattention.self.key.weight', 'text_encoder.encoder.layer.10.crossattention.self.query.bias', 'text_encoder.encoder.layer.10.crossattention.self.query.weight', 'text_encoder.encoder.layer.10.crossattention.self.value.bias', 'text_encoder.encoder.layer.10.crossattention.self.value.weight', 'text_encoder.encoder.layer.10.intermediate.dense.bias', 'text_encoder.encoder.layer.10.intermediate.dense.weight', 'text_encoder.encoder.layer.10.output.LayerNorm.bias', 'text_encoder.encoder.layer.10.output.LayerNorm.weight', 'text_encoder.encoder.layer.10.output.dense.bias', 'text_encoder.encoder.layer.10.output.dense.weight', 'text_encoder.encoder.layer.11.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.11.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.11.attention.output.dense.bias', 'text_encoder.encoder.layer.11.attention.output.dense.weight', 'text_encoder.encoder.layer.11.attention.self.key.bias', 'text_encoder.encoder.layer.11.attention.self.key.weight', 'text_encoder.encoder.layer.11.attention.self.query.bias', 'text_encoder.encoder.layer.11.attention.self.query.weight', 'text_encoder.encoder.layer.11.attention.self.value.bias', 'text_encoder.encoder.layer.11.attention.self.value.weight', 'text_encoder.encoder.layer.11.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.11.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.11.crossattention.output.dense.bias', 'text_encoder.encoder.layer.11.crossattention.output.dense.weight', 'text_encoder.encoder.layer.11.crossattention.self.key.bias', 'text_encoder.encoder.layer.11.crossattention.self.key.weight', 'text_encoder.encoder.layer.11.crossattention.self.query.bias', 'text_encoder.encoder.layer.11.crossattention.self.query.weight', 'text_encoder.encoder.layer.11.crossattention.self.value.bias', 'text_encoder.encoder.layer.11.crossattention.self.value.weight', 'text_encoder.encoder.layer.11.intermediate.dense.bias', 'text_encoder.encoder.layer.11.intermediate.dense.weight', 'text_encoder.encoder.layer.11.output.LayerNorm.bias', 'text_encoder.encoder.layer.11.output.LayerNorm.weight', 'text_encoder.encoder.layer.11.output.dense.bias', 'text_encoder.encoder.layer.11.output.dense.weight', 'text_encoder.encoder.layer.2.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.2.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.2.attention.output.dense.bias', 'text_encoder.encoder.layer.2.attention.output.dense.weight', 'text_encoder.encoder.layer.2.attention.self.key.bias', 'text_encoder.encoder.layer.2.attention.self.key.weight', 'text_encoder.encoder.layer.2.attention.self.query.bias', 'text_encoder.encoder.layer.2.attention.self.query.weight', 'text_encoder.encoder.layer.2.attention.self.value.bias', 'text_encoder.encoder.layer.2.attention.self.value.weight', 'text_encoder.encoder.layer.2.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.2.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.2.crossattention.output.dense.bias', 'text_encoder.encoder.layer.2.crossattention.output.dense.weight', 'text_encoder.encoder.layer.2.crossattention.self.key.bias', 'text_encoder.encoder.layer.2.crossattention.self.key.weight', 'text_encoder.encoder.layer.2.crossattention.self.query.bias', 'text_encoder.encoder.layer.2.crossattention.self.query.weight', 'text_encoder.encoder.layer.2.crossattention.self.value.bias', 'text_encoder.encoder.layer.2.crossattention.self.value.weight', 'text_encoder.encoder.layer.2.intermediate.dense.bias', 'text_encoder.encoder.layer.2.intermediate.dense.weight', 'text_encoder.encoder.layer.2.output.LayerNorm.bias', 'text_encoder.encoder.layer.2.output.LayerNorm.weight', 'text_encoder.encoder.layer.2.output.dense.bias', 'text_encoder.encoder.layer.2.output.dense.weight', 'text_encoder.encoder.layer.3.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.3.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.3.attention.output.dense.bias', 'text_encoder.encoder.layer.3.attention.output.dense.weight', 'text_encoder.encoder.layer.3.attention.self.key.bias', 'text_encoder.encoder.layer.3.attention.self.key.weight', 'text_encoder.encoder.layer.3.attention.self.query.bias', 'text_encoder.encoder.layer.3.attention.self.query.weight', 'text_encoder.encoder.layer.3.attention.self.value.bias', 'text_encoder.encoder.layer.3.attention.self.value.weight', 'text_encoder.encoder.layer.3.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.3.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.3.crossattention.output.dense.bias', 'text_encoder.encoder.layer.3.crossattention.output.dense.weight', 'text_encoder.encoder.layer.3.crossattention.self.key.bias', 'text_encoder.encoder.layer.3.crossattention.self.key.weight', 'text_encoder.encoder.layer.3.crossattention.self.query.bias', 'text_encoder.encoder.layer.3.crossattention.self.query.weight', 'text_encoder.encoder.layer.3.crossattention.self.value.bias', 'text_encoder.encoder.layer.3.crossattention.self.value.weight', 'text_encoder.encoder.layer.3.intermediate.dense.bias', 'text_encoder.encoder.layer.3.intermediate.dense.weight', 'text_encoder.encoder.layer.3.output.LayerNorm.bias', 'text_encoder.encoder.layer.3.output.LayerNorm.weight', 'text_encoder.encoder.layer.3.output.dense.bias', 'text_encoder.encoder.layer.3.output.dense.weight', 'text_encoder.encoder.layer.4.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.4.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.4.attention.output.dense.bias', 'text_encoder.encoder.layer.4.attention.output.dense.weight', 'text_encoder.encoder.layer.4.attention.self.key.bias', 'text_encoder.encoder.layer.4.attention.self.key.weight', 'text_encoder.encoder.layer.4.attention.self.query.bias', 'text_encoder.encoder.layer.4.attention.self.query.weight', 'text_encoder.encoder.layer.4.attention.self.value.bias', 'text_encoder.encoder.layer.4.attention.self.value.weight', 'text_encoder.encoder.layer.4.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.4.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.4.crossattention.output.dense.bias', 'text_encoder.encoder.layer.4.crossattention.output.dense.weight', 'text_encoder.encoder.layer.4.crossattention.self.key.bias', 'text_encoder.encoder.layer.4.crossattention.self.key.weight', 'text_encoder.encoder.layer.4.crossattention.self.query.bias', 'text_encoder.encoder.layer.4.crossattention.self.query.weight', 'text_encoder.encoder.layer.4.crossattention.self.value.bias', 'text_encoder.encoder.layer.4.crossattention.self.value.weight', 'text_encoder.encoder.layer.4.intermediate.dense.bias', 'text_encoder.encoder.layer.4.intermediate.dense.weight', 'text_encoder.encoder.layer.4.output.LayerNorm.bias', 'text_encoder.encoder.layer.4.output.LayerNorm.weight', 'text_encoder.encoder.layer.4.output.dense.bias', 'text_encoder.encoder.layer.4.output.dense.weight', 'text_encoder.encoder.layer.5.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.5.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.5.attention.output.dense.bias', 'text_encoder.encoder.layer.5.attention.output.dense.weight', 'text_encoder.encoder.layer.5.attention.self.key.bias', 'text_encoder.encoder.layer.5.attention.self.key.weight', 'text_encoder.encoder.layer.5.attention.self.query.bias', 'text_encoder.encoder.layer.5.attention.self.query.weight', 'text_encoder.encoder.layer.5.attention.self.value.bias', 'text_encoder.encoder.layer.5.attention.self.value.weight', 'text_encoder.encoder.layer.5.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.5.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.5.crossattention.output.dense.bias', 'text_encoder.encoder.layer.5.crossattention.output.dense.weight', 'text_encoder.encoder.layer.5.crossattention.self.key.bias', 'text_encoder.encoder.layer.5.crossattention.self.key.weight', 'text_encoder.encoder.layer.5.crossattention.self.query.bias', 'text_encoder.encoder.layer.5.crossattention.self.query.weight', 'text_encoder.encoder.layer.5.crossattention.self.value.bias', 'text_encoder.encoder.layer.5.crossattention.self.value.weight', 'text_encoder.encoder.layer.5.intermediate.dense.bias', 'text_encoder.encoder.layer.5.intermediate.dense.weight', 'text_encoder.encoder.layer.5.output.LayerNorm.bias', 'text_encoder.encoder.layer.5.output.LayerNorm.weight', 'text_encoder.encoder.layer.5.output.dense.bias', 'text_encoder.encoder.layer.5.output.dense.weight', 'text_encoder.encoder.layer.6.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.6.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.6.attention.output.dense.bias', 'text_encoder.encoder.layer.6.attention.output.dense.weight', 'text_encoder.encoder.layer.6.attention.self.key.bias', 'text_encoder.encoder.layer.6.attention.self.key.weight', 'text_encoder.encoder.layer.6.attention.self.query.bias', 'text_encoder.encoder.layer.6.attention.self.query.weight', 'text_encoder.encoder.layer.6.attention.self.value.bias', 'text_encoder.encoder.layer.6.attention.self.value.weight', 'text_encoder.encoder.layer.6.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.6.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.6.crossattention.output.dense.bias', 'text_encoder.encoder.layer.6.crossattention.output.dense.weight', 'text_encoder.encoder.layer.6.crossattention.self.key.bias', 'text_encoder.encoder.layer.6.crossattention.self.key.weight', 'text_encoder.encoder.layer.6.crossattention.self.query.bias', 'text_encoder.encoder.layer.6.crossattention.self.query.weight', 'text_encoder.encoder.layer.6.crossattention.self.value.bias', 'text_encoder.encoder.layer.6.crossattention.self.value.weight', 'text_encoder.encoder.layer.6.intermediate.dense.bias', 'text_encoder.encoder.layer.6.intermediate.dense.weight', 'text_encoder.encoder.layer.6.output.LayerNorm.bias', 'text_encoder.encoder.layer.6.output.LayerNorm.weight', 'text_encoder.encoder.layer.6.output.dense.bias', 'text_encoder.encoder.layer.6.output.dense.weight', 'text_encoder.encoder.layer.7.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.7.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.7.attention.output.dense.bias', 'text_encoder.encoder.layer.7.attention.output.dense.weight', 'text_encoder.encoder.layer.7.attention.self.key.bias', 'text_encoder.encoder.layer.7.attention.self.key.weight', 'text_encoder.encoder.layer.7.attention.self.query.bias', 'text_encoder.encoder.layer.7.attention.self.query.weight', 'text_encoder.encoder.layer.7.attention.self.value.bias', 'text_encoder.encoder.layer.7.attention.self.value.weight', 'text_encoder.encoder.layer.7.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.7.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.7.crossattention.output.dense.bias', 'text_encoder.encoder.layer.7.crossattention.output.dense.weight', 'text_encoder.encoder.layer.7.crossattention.self.key.bias', 'text_encoder.encoder.layer.7.crossattention.self.key.weight', 'text_encoder.encoder.layer.7.crossattention.self.query.bias', 'text_encoder.encoder.layer.7.crossattention.self.query.weight', 'text_encoder.encoder.layer.7.crossattention.self.value.bias', 'text_encoder.encoder.layer.7.crossattention.self.value.weight', 'text_encoder.encoder.layer.7.intermediate.dense.bias', 'text_encoder.encoder.layer.7.intermediate.dense.weight', 'text_encoder.encoder.layer.7.output.LayerNorm.bias', 'text_encoder.encoder.layer.7.output.LayerNorm.weight', 'text_encoder.encoder.layer.7.output.dense.bias', 'text_encoder.encoder.layer.7.output.dense.weight', 'text_encoder.encoder.layer.8.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.8.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.8.attention.output.dense.bias', 'text_encoder.encoder.layer.8.attention.output.dense.weight', 'text_encoder.encoder.layer.8.attention.self.key.bias', 'text_encoder.encoder.layer.8.attention.self.key.weight', 'text_encoder.encoder.layer.8.attention.self.query.bias', 'text_encoder.encoder.layer.8.attention.self.query.weight', 'text_encoder.encoder.layer.8.attention.self.value.bias', 'text_encoder.encoder.layer.8.attention.self.value.weight', 'text_encoder.encoder.layer.8.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.8.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.8.crossattention.output.dense.bias', 'text_encoder.encoder.layer.8.crossattention.output.dense.weight', 'text_encoder.encoder.layer.8.crossattention.self.key.bias', 'text_encoder.encoder.layer.8.crossattention.self.key.weight', 'text_encoder.encoder.layer.8.crossattention.self.query.bias', 'text_encoder.encoder.layer.8.crossattention.self.query.weight', 'text_encoder.encoder.layer.8.crossattention.self.value.bias', 'text_encoder.encoder.layer.8.crossattention.self.value.weight', 'text_encoder.encoder.layer.8.intermediate.dense.bias', 'text_encoder.encoder.layer.8.intermediate.dense.weight', 'text_encoder.encoder.layer.8.output.LayerNorm.bias', 'text_encoder.encoder.layer.8.output.LayerNorm.weight', 'text_encoder.encoder.layer.8.output.dense.bias', 'text_encoder.encoder.layer.8.output.dense.weight', 'text_encoder.encoder.layer.9.attention.output.LayerNorm.bias', 'text_encoder.encoder.layer.9.attention.output.LayerNorm.weight', 'text_encoder.encoder.layer.9.attention.output.dense.bias', 'text_encoder.encoder.layer.9.attention.output.dense.weight', 'text_encoder.encoder.layer.9.attention.self.key.bias', 'text_encoder.encoder.layer.9.attention.self.key.weight', 'text_encoder.encoder.layer.9.attention.self.query.bias', 'text_encoder.encoder.layer.9.attention.self.query.weight', 'text_encoder.encoder.layer.9.attention.self.value.bias', 'text_encoder.encoder.layer.9.attention.self.value.weight', 'text_encoder.encoder.layer.9.crossattention.output.LayerNorm.bias', 'text_encoder.encoder.layer.9.crossattention.output.LayerNorm.weight', 'text_encoder.encoder.layer.9.crossattention.output.dense.bias', 'text_encoder.encoder.layer.9.crossattention.output.dense.weight', 'text_encoder.encoder.layer.9.crossattention.self.key.bias', 'text_encoder.encoder.layer.9.crossattention.self.key.weight', 'text_encoder.encoder.layer.9.crossattention.self.query.bias', 'text_encoder.encoder.layer.9.crossattention.self.query.weight', 'text_encoder.encoder.layer.9.crossattention.self.value.bias', 'text_encoder.encoder.layer.9.crossattention.self.value.weight', 'text_encoder.encoder.layer.9.intermediate.dense.bias', 'text_encoder.encoder.layer.9.intermediate.dense.weight', 'text_encoder.encoder.layer.9.output.LayerNorm.bias', 'text_encoder.encoder.layer.9.output.LayerNorm.weight', 'text_encoder.encoder.layer.9.output.dense.bias', 'text_encoder.encoder.layer.9.output.dense.weight', 'text_proj.bias', 'text_proj.weight', 'vision_proj.bias', 'vision_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BlipForImageTextRetrieval(\n",
       "  (vision_model): BlipVisionModel(\n",
       "    (embeddings): BlipVisionEmbeddings(\n",
       "      (patch_embedding): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
       "    )\n",
       "    (encoder): BlipEncoder(\n",
       "      (layers): ModuleList(\n",
       "        (0-11): 12 x BlipEncoderLayer(\n",
       "          (self_attn): BlipAttention(\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (projection): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): BlipMLP(\n",
       "            (activation_fn): GELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (post_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (text_encoder): BlipTextModel(\n",
       "    (embeddings): BlipTextEmbeddings(\n",
       "      (word_embeddings): Embedding(30524, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.0, inplace=False)\n",
       "    )\n",
       "    (encoder): BlipTextEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-11): 12 x BlipTextLayer(\n",
       "          (attention): BlipTextAttention(\n",
       "            (self): BlipTextSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): BlipTextSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (crossattention): BlipTextAttention(\n",
       "            (self): BlipTextSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): BlipTextSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BlipTextIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BlipTextOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (vision_proj): Linear(in_features=768, out_features=256, bias=True)\n",
       "  (text_proj): Linear(in_features=768, out_features=256, bias=True)\n",
       "  (itm_head): Linear(in_features=768, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch.optim import AdamW\n",
    "from transformers import BlipForImageTextRetrieval\n",
    "blip_path = \"/root/autodl-fs/blip-image-captioning-base/\"\n",
    "\n",
    "model = BlipForImageTextRetrieval.from_pretrained(blip_path).cuda()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1542f5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from transformers import AutoProcessor\n",
    "\n",
    "processor = AutoProcessor.from_pretrained(blip_path, use_fast=True)\n",
    "image_path = df[\"path\"][0]\n",
    "text = df[\"caption\"][0]\n",
    "image = Image.open(image_path).convert('RGB')\n",
    "inputs = processor(\n",
    "    images=image,\n",
    "    text=text,\n",
    "    return_tensors=\"pt\"\n",
    ").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b36d54f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.0800], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# 4. 获取原始的768维特征(importent)\n",
    "with torch.no_grad():\n",
    "    # 获取视觉特征(768维)\n",
    "    vision_outputs = model.vision_model(inputs.pixel_values)\n",
    "    vision_embeds = vision_outputs.last_hidden_state[:, 0, :]  # [batch_size, 768]\n",
    "    \n",
    "    # 获取文本特征(768维)\n",
    "    text_outputs = model.text_encoder(\n",
    "        input_ids=inputs.input_ids,\n",
    "        attention_mask=inputs.attention_mask\n",
    "    )\n",
    "    text_embeds = text_outputs.last_hidden_state[:, 0, :]  # [batch_size, 768]\n",
    "    \n",
    "    # 使用投影层将特征从768维映射到256维\n",
    "    vision_projected = model.vision_proj(vision_embeds)  # [batch_size, 256]\n",
    "    text_projected = model.text_proj(text_embeds)      # [batch_size, 256]\n",
    "    # text_projected和text_embeds都是tensor类型，请计算余弦相似度\n",
    "    \n",
    "    # 计算余弦相似度\n",
    "    cosine_similarity = torch.nn.functional.cosine_similarity(vision_projected, text_projected, dim=1)\n",
    "    print(cosine_similarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17d1ad2b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "multimodel",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
